Investing in late-stage clinical trials and manufacturing of product candidates for five major infectious diseases: a modelling study of the benefits and costs of investment in three middle-income countries

Summary Background Investing in late-stage clinical trials, trial sites, and production capacity for new health products could improve access to vaccines, therapeutics, and infectious disease diagnostics in middle-income countries. This study assesses the case for such investment in three of these countries: India, Kenya, and South Africa. Methods We applied investment case modelling and assessed how many cases, deaths, and disability-adjusted life years (DALYs) could be averted from the development and manufacturing of new technologies (therapeutics and vaccines) in these countries from 2021 to 2036, for five diseases—HIV, tuberculosis, malaria, pneumonia, and diarrhoeal diseases. We also estimated the economic benefits that might accrue from making these investments and we developed benefit–cost ratios for each of the three middle-income countries. Our modelling applies two investment case perspectives: a societal perspective with all costs and benefits measured at the societal level, and a country perspective to estimate how much health and economic benefit accrues to each middle-income country for every dollar invested in clinical trials and manufacturing by the middle-income country government. For each perspective, we modelled two scenarios: one that considers only domestic health and economic benefits; and one that includes regional health and economic benefits. In the regional scenarios, we assumed that new products developed and manufactured in India would benefit eight countries in south Asia, whereas new products developed and manufactured in Kenya would benefit all 21 countries in the Common Market for Eastern and Southern Africa (COMESA). We also assumed that all 16 countries in the Southern African Development Community (SADC) would benefit from products developed and manufactured in South Africa. Findings From 2021 to 2036, product development and manufacturing in Kenya could avert 4·44 million deaths and 206·27 million DALYs in the COMESA region. In South Africa, it could prevent 5·19 million deaths and 253·83 million DALYs in the SADC region. In India, it could avert 9·76 million deaths and 374·42 million DALYs in south Asia. Economic returns would be especially high if new tools were produced for regional markets rather than for domestic markets only. Under a societal perspective, regional returns outweigh investments by a factor of 20·51 in Kenya, 33·27 in South Africa, and 66·56 in India. Under a country perspective, the regional benefit–cost ratios amount to 60·71 in India, 8·78 in Kenya, and 11·88 in South Africa. Interpretation Our study supports the creation of regional hubs for clinical trials and product manufacturing compared with narrow national efforts. Funding Bill & Melinda Gates Foundation.


Annex 2: Model assumptions
Building clinical trial capacity A one-time cost will be incurred per trial site, to build and equip the trial site (Source: Authors' calculation based on literature review). Building clinical trial capacity Once a site is built, a recurring annual cost will be incurred per site to maintain and further develop capacity (Source: Authors' calculation based on literature review). Building clinical trial capacity Countries will make a recurring annual investment in clinical infectious disease research training to sustain capacity (Source: Authors' calculation based on literature review). Strengthening NRAs Countries will invest in regulation to ensure that its ratio of total pharmaceutical regulation spending to total pharmaceutical market size is equal to the average ratio among the countries with the top 10 largest pharmaceutical markets (Source: Authors' calculation based on literature review).

Clinical trials
Costs for phase III trials for vaccines and drugs included (Source: P2I).
Clinical trials Phase III trials for at least two candidate products of the same product-archetype are sufficient to yield one product launch. P2I indicates a transition probability >50% for drugs and vaccines (Source: P2I). Clinical trials Average drug and vaccine clinical trial duration is three years (Data source: P2I).
Clinical trials One vaccine and one new drug will be developed for HIV, TB, malaria, pneumonia. For diarrhea, a new drug will be developed. Clinical trials A country will start one trial per product per year, until the total number of trials started is enough to guarantee one launch per product. In this case, two years.

Clinical trials
Each trial site can only run one clinical trial at a time.

Clinical trials
No lag between investing in clinical trial capacity and starting a clinical trial.

Manufacturing capacity
Total investment of $250 million investment per country for strengthening manufacturing capacity. Equivalent to building three new plants for vaccines and drugs each (Source: Authors' calculation based on literature review).

Health benefits
New vaccine launch will reduce the annual regional or domestic incidence of a disease by 10 percentage points per year, until a maximum reduction of 90% reduction in incidence from the protective effect of vaccines is reached. Health benefits Vaccine efficacy: We assume a successfully developed vaccine will be 65% effective in preventing the disease.

Health benefits
New drug launch will increase regional or domestic treatment coverage for a disease by 10 percentage points per year, until a coverage of 90% is reached. Economic returns (general) A product will launch in the year following the year of clinical trial completion (i.e., if clinical trial ends in 2023, product will launch in 2024). Economic returns (trial site fees) Countries/investors will receive a fixed annual return per trial site, for each year that a trial site is in use. This return represents the amount paid to the investor by a contract research organization.

Economic returns (product sales)
Each product launch will generate profits in the form of product sales to the domestic or regional market (COMESA; SADC; south Asia). Product sales are only accrued from doses manufactured in the newly created production plants (total volume: 90 million vaccines and drug doses per year). Economic returns (IP & tech transfer royalties) Each product launch will result in one biopharmaceutical license that involves a return from tech transfer/IP royalties. Tech transfer royalties are calculated as 5.0% of product sales resulting from tech transfer arrangements with manufacturers from other countries. Benefits from tech transfers were only included when demand for the successful candidate exceeded the newly installed manufacturing capacity of 90 million vaccine doses and 90 million drug doses per year. Economic returns (net treatment costs averted)* Treatment costs averted were calculated as the product of cases averted and treatment cost per case, minus the sum of the cost of new cases treated and the cost of vaccine procurement. 4 Technology transfer royalties as a percentage of product sales 11.69% (1.25-48.80) Borshell, N., Dawkes

Equation 1
Where YLL is the average years of life lost per death, a is age group, D is number of deaths per year, and L is life expectancy.

Average years of life lost to disability per non-treated case
Equation 2 Where YLD is the average years of life lost to disability per non-treated case, a is age group, I is annual incidence, T is disease duration without treatment expressed in years, DW is the disability weight. All disability weights were collected from the IHME database.

Average years of life lost to disability per treated case
Equation 3 Where YLD is the average years of life lost to disability per treated case, a is age group, I is annual incidence, T is disease duration with treatment expressed in years, DW is the disability weight. All disability weights were collected from the IHME database.

Number of cases averted
Where N is the number of cases averted, i is the disease, x is the year, IB is the baseline incidence of disease, and IV is the incidence of disease with a new vaccine. Where N is the number of deaths averted, i is the disease, x is the year, IB is the baseline incidence, CB is the baseline treatment coverage, CFR is the case fatality rate without treatment, CFRT is the case fatality rate with treatment, IV is the incidence with a new vaccine, and CD is the treatment coverage with a new drug.

Number of years of life lost to death averted
Where N is the number of years of life lost to death averted, i is the disease, x is the year, DB is the baseline number of deaths, YLL is the average years of life lost per death, and DT is the number of deaths with a new vaccine and drug.

Number of years of life lost to disability averted
Where N is the number of years of life lost to disability averted, i is the disease, x is the year, IB is the baseline incidence, CB is the baseline treatment coverage, YLD is the average number of years of life lost to disability per non-treated case, YLDT is the average number of years of life lost to disability per treated case, IV is the incidence with a new vaccine, and CD is the treatment coverage with a new drug.

Number of disability adjusted life years averted
Where N is the number of disability adjusted life years averted, i is the disease, x is the year, YLL is the number of years of life lost to death averted, and YLD is the number of years of life lost to disability averted.

Treatment costs averted
Where C is treatment costs averted, i is the disease, x is the year, N is the number of cases averted, and K is the cost per case treated.

Equation 10
Where N is number of vaccine doses needed, i is the disease, x is the year, A is the number of cases averted, and E is vaccine efficacy.

Equation 11
Where C is vaccine procurement costs, i is the disease, x is the year, N is the number of vaccine doses needed, K is the procurement cost per vaccine dose.

Equation 12
Where N is number of new cases treated, i is the disease, x is the year, IV is the incidence with a new vaccine, CD is the treatment coverage with a new drug, IB is the baseline incidence, CB is the baseline treatment coverage.

Equation 13
Where C is cost of new cases treated, i is the disease, x is the year, N is the number of new cases treated, K is the cost per case treated.

Equation 14
Where C is clinical trial site start-up costs, i is the disease, x is the year, T is the number of new clinical trial sites started, and K is the startup cost per clinical trial site.

Equation 15
Where C is clinical trial site maintenance costs, i is disease, x is year, T is the number of new clinical trial sites in existence, and K is the annual cost to maintain one clinical trial site.

Equation 16
Where C is late-stage clinical trial costs, i is the disease, x is the year, T is the number of clinical trials started, and K is the cost per phase 3 clinical trial.

Equation 17
Where C is clinical trial training costs, x is the year, and K is the annual investment in clinical trial training.

Equation 18
Where R is returns from trial site user fees, i is the disease, x is the year, T is the number of clinical trial sites in use, and C is the annual cost to use a clinical trial site.

Equation 19
Where R is returns from product sales, i is the disease, x is the year, V is the number of vaccines manufactured nationally, CV is the cost per vaccine dose, D is the number of drugs manufactured nationally, CD is the cost per drug dose, and M is the profit margin on vaccine and drug sales.

Equation 20
Where R is returns from tech transfer, i is the disease, x is the year, V is the number of vaccines manufactured through tech transfer, CV is the cost per vaccine dose, D is the number of drugs manufactured through tech transfer, CD is the cost per drug dose, M is the profit margin on vaccine and drug sales, and Y is the royalty rate on the sale vaccines and drugs manufactured through tech transfer.

Equation 21
Where K is the investment in health regulation, x is the year, R is the average ratio of pharmaceutical regulation spending to total pharmaceutical market size of the countries with the top ten largest pharmaceutical markets, S is the current pharmaceutical market size of the country of interest, and A is current spending on pharmaceutical regulation in the country of interest.

Economic productivity
= ∑ ∑ [( , * , * * ) + ( , * , * * ) Equation 22 Where E is total economic productivity, i is the disease, x is the year, YLL is the years of life lost to death averted, YLD is the years of life lost to disability averted, D is the percent of deaths age 15 to 69, C is the percent of cases age 15 to 69, R is the employment rate, and W is the minimum wage.

Annex 6. Sensitivity analysis results (BCRs), US$2021
We conducted six sensitivity analyses (SAs) to account for uncertainty in our parameter estimates. The standard 3.0% discount rate used in global health economic evaluations may be inconsistent with low-and middle-income economies, so we increased the discount rate to 5.0% (SA 1). 1 We also increased the proportion of phase III clinical trial costs and manufacturing costs covered by countries from 10% to 20% due to a limited availability of data regarding government cost contributions to clinical trials and manufacturing (SA 2). To further account for limited data on government costs contributions to clinical trials and manufacturing, we conducted an additional sensitivity analysis where we increased government coverage of phase III trial costs and manufacturing costs from 10% to 50% (SA 3). To account for potentially low estimates of phase III clinical trial costs presented in the P2I model, we increased all phase III trial costs by 10% (SA 4). We also reduced the P2I phase III transition probabilities from average to minimum values to account for potential inefficiencies in clinical trial designs (SA 5).
Lastly, while the major focus of our study was on the direct financial gains that result from investments in clinical trial and manufacturing, we also added economic productivity to the societal perspective, to capture the longer-term benefits of these investments (SA 6). Economic productivity was calculated by monetizing YLLs averted and YLDs averted (Annex 3, Equation 22). Economic productivity from YLLs averted was calculated as the product of total YLLs averted among the working population and annual minimum wage. Similarly, economic productivity from YLDs averted was calculated as the product of total YLDs averted among the working population and annual minimum wage. Annual minimum wage estimates and employment rates used to define the working population can be found in Annex 2. In South Africa's regional scenario, HIV accounts for 53.6%, malaria 21.0%, pneumonia 10.9%, TB 9.6%, and diarrhea 4.9% of all DALYs averted. Generally, the magnitude of DALYs averted for each disease reflects both the national disease burden, health seeking behaviors in each country, and the disability weights of each disease. In India, for example, the incidence of pneumonia is much greater than HIV, TB, and malaria, but less than the incidence of diarrhea. However, pneumonia has a higher number of YLDs per case than diarrhea. Consequently, pneumonia accounts for a majority of the DALYs averted in India. Similarly, in South Africa, the incidence of HIV is slightly more than that of TB and malaria, but much less than the incidence of pneumonia and diarrhea. However, HIV has a much larger number of YLDs per case than pneumonia and diarrhea. Consequently, HIV accounts for a majority of DALYs averted in South Africa. Figure A1. DALYs averted stratified by disease from the India domestic, societal perspective. The DALYs averted in any given year are cumulative across diseases, but not across previous years.  Figure A6. DALYs averted stratified by disease from the South Africa regional, societal perspective. The DALYs averted in any given year are cumulative across diseases, but not across previous years.

Economic Benefits
Our results show that the economic benefits of investments in clinical trial and manufacturing capacity are not distributed evenly across the diseases. In our model, averted treatment costs are a product of averted cases. Additionally, cases are only averted through new vaccines and not new drugs. Since we do not model a new vaccine for diarrheal diseases, there are no averted cases of diarrhea and therefore no averted treatment costs for diarrhea.
In India's domestic scenario, for example, pneumonia accounts for 82.6%, TB 15.6%, malaria 1.6%, and HIV 0.2% of all treatment costs averted. India's regional scenario has a similar distribution of treatment costs averted across diseases. In Kenya's domestic scenario, pneumonia accounts for 46.3%, TB 27.2%, malaria 23.8%, and HIV 2.7% of all treatment costs averted. The distribution is similar in the regional scenario. In South Africa's domestic scenario, pneumonia accounts for 69.1%, HIV 16.9%, TB 13.7%, and malaria 0.3% of all treatment costs averted. In South Africa's regional scenario pneumonia accounts for 70.7%, malaria 17.5%, TB 6.8%, and HIV 5.0% of all treatment costs averted. Generally, the magnitude of treatment costs averted through investments in each disease reflect the disease burden in each country. For example, pneumonia's large contribution to treatment costs averted is a result of both its high cost of treatment and high incidence in all three countries, relative to the other diseases. Similarly, in South Africa where the burden of HIV is the highest in the world, HIV is a large contributor to treatment costs averted in the domestic scenario, but the smallest contributor to treatment costs averted in the regional scenario as the burden of HIV in surrounding countries in comparatively small.  Figure A7. Treatment costs averted stratified by disease from the India domestic, societal perspective. The treatment costs averted in any given year are cumulative across diseases, but not across previous years. Figure A8. Treatment costs averted stratified by disease from the India regional, societal perspective. The treatment costs averted in any given year are cumulative across diseases, but not across previous years. Figure A9. Treatment costs averted stratified by disease from the Kenya domestic, societal perspective. The treatment costs averted in any given year are cumulative across diseases, but not across previous years. Figure A10. Treatment costs averted stratified by disease from the Kenya regional, societal perspective. The treatment costs averted in any given year are cumulative across diseases, but not across previous years.  Figure A11. Treatment costs averted stratified by disease from the South Africa domestic, societal perspective. The treatment costs averted in any given year are cumulative across diseases, but not across previous years. Figure A12. Treatment costs averted stratified by disease from the South Africa regional, societal perspective. The treatment costs averted in any given year are cumulative across diseases, but not across previous years.